Do you know how Target, a major American retailer, predicted the pregnancy of one of its customers or how Netflix was sure that the series "House of Cards" would be a success even before it premiered?
The answer lies in data, more specifically in Big Data, a kind of mountain of data that computer programmes based on Artificial Intelligence and/or Machine Learning (powered by powerful analysis algorithms) capture, organise and process so that they can be interpreted using mathematical and statistical methods.
By analysing the behaviour of customers' consumption habits, valuable information can be obtained that makes it possible to create winning business strategies, even before they are created. The two cases mentioned above are paradigmatic of how the use of this data can be transformed into information and help companies make decisions.
Data: what it is and how important it is
We live in the Information Age and, as Steve Jobs used to say, "information is power". This power is immortalised in the expression "Data is the new Oil".
Coined in 2006, this expression has since gained weight and frequency in the press and is referred to by CEOs and world leaders. It's an analogy, because in the same way that a century ago, companies that managed to exploit oil accumulated vast wealth and built the basis of a new type of economy, today, data-driven companies like Google, Facebook, Uber, Amazon or Yahoo are doing the same.
However, these giants are not the only ones using data to improve their performance. With or without an online presence, companies of all sizes and sectors have realised that they need to anticipate consumer trends in order to stay one step ahead of the competition and win over customers.
A name, a LikeA post on a social network, an address or a mobile phone number left on an online form is data. While money is the mediator in the purchase of a good or product, the information that consumers leave online is the fuel that feeds the set of strategies for attracting and retaining customers that we call Marketing and which is so important in leveraging the various types of business.
Back to the Target example. After analysing which products were most bought by pregnant customers, the company drew up a typical profile that allowed it not only to understand in advance which customers were pregnant, but also to design campaigns specifically aimed at mothers-to-be, thus getting ahead of the competition.
As we can see from the example above, the mere availability of data does not guarantee the assertiveness of the information, nor does it clearly reveal the solution to each company's problems. "Raw" data is of little use; only after it has been debugged and refined can this information become an asset for a company.
Businesses that are unable to capture, add and translate different types of data will find it difficult to devise strategies and establish effective, personalised communication with their customers.
Types of data analysis
In order to extract "knowledge" from the data it stores, a business will first need to understand which type of data analysis best serves its objectives. Not all analyses are the same. The most common analysis methodologies are descriptive, diagnostic, predictive and prescriptive, and each one is linked to a specific objective.
For example, if a retailer wants to know the reason behind an extraordinary increase in the number of visitors to their online or physical shop over a limited period of time, diagnostic analysis is the most suitable, but if that same retailer is interested in predicting whether this behaviour is likely to continue in the future, predictive analysis, which relates causes and effects (data), is the most suitable.
The objectives of each business indicate the adoption or abandonment of a particular analysis model, but the relevance of data processing remains the same. By analysing the data, businesses can understand what kind of products, sales channels or payment methods are becoming consumer trends and take the necessary measures to ensure that the shopping experience meets the customer's needs and expectations at all times.
Business information solutions
As you can see, the data and its analysis are extremely useful and highlight the role that knowledge solutions such as the REDUNIQ Insights have on the economic viability of businesses.
Developed by REDUNIQ (the largest national and foreign card acceptance network in Portugal), REDUNIQ Insights is a knowledge solution that aims to provide analyses based on information on national retail activityIt supports companies in generating insights and making business development decisions.
Using analytical information based on transactional data from the REDUNIQ payment acceptance network in Portugal, this solution prepares information reports for companies personalised and tailored to each company's reality. Anticipating trends means a huge competitive advantage for companies. This invariably involves getting to know "their" consumers better.
With REDUNIQ Insights this is possible
Knowing who they are, where they live or come from, where they spend the most, who is consuming products similar to yours, who you can communicate with or even compare your business with the sector in general, are some of the fundamental elements for a company's growth that REDUNIQ Insights allows you to know.
For example, using REDUNIQ Insights, a business with several shops across Portugal can find out, among other things, what percentage of turnover in each of its spaces corresponds to contactless payments - transactions made by approaching a card or wearable to a contactless POS, or if the online payment solutions that you offer on your e-commerce platform are in line with current consumer trends.
Find out more about the REDUNIQ Insights solution and the reports already available from REDUNIQ:
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